Node Classification on Cora (Accuracy, ECE, NLL, BS, AUROC)
97.91AccuracyVecFormer
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| VecFormer2026.02 | 97.91 | — | — | — | — | |
| SCL-GNNBackbone=GAT2026.03 | 97.9 | — | — | — | — | |
| CANETBackbone=GAT2026.03 | 97.35 | — | — | — | — | |
| SCL-GNNBackbone=GCN2026.03 | 97.14 | — | — | — | — | |
| Exphormer2026.02 | 96.35 | — | — | — | — | |
| SGFormer2026.02 | 96.29 | — | — | — | — | |
| CANETBackbone=GCN2026.03 | 96.25 | — | — | — | — | |
| CaNetbackbone=GAT2026.02 | 95.94 | — | — | — | — | |
| Coralbackbone=GAT2026.02 | 95.74 | — | — | — | — | |
| IRMbackbone=GAT2026.02 | 95.72 | — | — | — | — | |
| DANNbackbone=GAT2026.02 | 95.66 | — | — | — | — | |
| ERMbackbone=GAT2026.02 | 95.57 | — | — | — | — | |
| GroupDRObackbone=GAT2026.02 | 95.38 | — | — | — | — | |
| SRGNNbackbone=GAT2026.02 | 95.36 | — | — | — | — | |
| Polynormer2026.02 | 94.96 | — | — | — | — | |
| Mixupbackbone=GAT2026.02 | 94.66 | — | — | — | — | |
| GAT + G-ΔUQBackbone=GAT2026.02 | 93.9 | 0.015 | 0.211 | 0.097 | 87.2 | |
| GAT + SIGHTBackbone=GAT2026.02 | 93.9 | 0.017 | 0.227 | 0.097 | 87.1 | |
| GCN + SIGHTBackbone=GCN2026.02 | 93.2 | 0.015 | 0.241 | 0.105 | 86.7 | |
| SRGNNBackbone=GAT2026.03 | 93.2 | — | — | — | — | |
| SRGNNBackbone=GCN2026.03 | 91.57 | — | — | — | — | |
| EERMbackbone=GAT2026.02 | 91.37 | — | — | — | — | |
| GCN + G-ΔUQBackbone=GCN2026.02 | 91.3 | 0.019 | 0.27 | 0.13 | 87.3 | |
| GATBackbone=GAT2026.02 | 90.6 | 0.015 | 0.286 | 0.137 | 88.9 | |
| EERMBackbone=GAT2026.03 | 89.85 | — | — | — | — | |
| GCNBackbone=GCN2026.02 | 89.8 | 0.019 | 0.318 | 0.15 | 88.7 | |
| EERMBackbone=GCN2026.03 | 88.57 | — | — | — | — | |
| NodeFormer2026.02 | 88.48 | — | — | — | — | |
| StableGNNBackbone=GAT2026.03 | 84.67 | — | — | — | — | |
| NAGphormer2026.02 | 84.35 | — | — | — | — | |
| StableGNNBackbone=GCN2026.03 | 84.05 | — | — | — | — |